TY - JOUR
T1 - Temporal Features of Muscle Synergies in Sit-to-Stand Motion Reflect the Motor Impairment of Post-Stroke Patients
AU - Yang, Ningjia
AU - An, Qi
AU - Kogami, Hiroki
AU - Yamakawa, Hiroshi
AU - Tamura, Yusuke
AU - Takahashi, Kouji
AU - Kinomoto, Makoto
AU - Yamasaki, Hiroshi
AU - Itkonen, Matti
AU - Shibata-Alnajjar, Fady
AU - Shimoda, Shingo
AU - Hattori, Noriaki
AU - Fujii, Takanori
AU - Otomune, Hironori
AU - Miyai, Ichiro
AU - Yamashita, Atsushi
AU - Asama, Hajime
N1 - Funding Information:
Manuscript received March 15, 2019; revised June 19, 2019 and August 21, 2019; accepted August 27, 2019. Date of publication September 4, 2019; date of current version October 8, 2019. This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant 26120005, Grant 16H04293, Grant 18H01405, Grant 19K22799, and Grant 19H05729. (Corresponding author: Qi An.) N. Yang, Q. An, H. Kogami, H. Yamakawa, Y. Tamura, A. Yamashita, and H. Asama are with the Department of Precision Engineering, The University of Tokyo, Tokyo 113-8656, Japan (e-mail: anqi@ robot.t.u-tokyo.ac.jp).
Publisher Copyright:
© 2001-2011 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Sit-to-stand (STS) motion is an important daily activity, and many post-stroke patients have difficulty performing STS motion. Previous studies found that there are four muscle synergies (synchronized muscle activations) in the STS motion of healthy adults. However, for post-stroke patients, it is unclear whether muscle synergies change and which features primarily reflect motor impairment. Here, we use a machine learning method to demonstrate that temporal features in two muscle synergies that contribute to hip rising and balance maintenance motion reflect the motor impairment of post-stroke patients. Analyzing the muscle synergies of age-matched healthy elderly people ( $n = 12$ ) and post-stroke patients ( $n = 33$ ), we found that the same four muscle synergies could account for the muscle activity of post-stroke patients. Also, we were able to distinguish post-stroke patients from healthy people on the basis of the temporal features of these muscle synergies. Furthermore, these temporal features were found to correlate with motor impairment of post-stroke patients. We conclude that post-stroke patients can still utilize the same number of muscle synergies as healthy people, but the temporal structure of muscle synergies changes as a result of motor impairment. This could lead to a new rehabilitation strategy for post-stroke patients that focuses on activation timing of muscle synergies.
AB - Sit-to-stand (STS) motion is an important daily activity, and many post-stroke patients have difficulty performing STS motion. Previous studies found that there are four muscle synergies (synchronized muscle activations) in the STS motion of healthy adults. However, for post-stroke patients, it is unclear whether muscle synergies change and which features primarily reflect motor impairment. Here, we use a machine learning method to demonstrate that temporal features in two muscle synergies that contribute to hip rising and balance maintenance motion reflect the motor impairment of post-stroke patients. Analyzing the muscle synergies of age-matched healthy elderly people ( $n = 12$ ) and post-stroke patients ( $n = 33$ ), we found that the same four muscle synergies could account for the muscle activity of post-stroke patients. Also, we were able to distinguish post-stroke patients from healthy people on the basis of the temporal features of these muscle synergies. Furthermore, these temporal features were found to correlate with motor impairment of post-stroke patients. We conclude that post-stroke patients can still utilize the same number of muscle synergies as healthy people, but the temporal structure of muscle synergies changes as a result of motor impairment. This could lead to a new rehabilitation strategy for post-stroke patients that focuses on activation timing of muscle synergies.
KW - Muscle synergy
KW - post-stroke
KW - random forest
KW - rehabilitation
KW - sit-to-stand
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U2 - 10.1109/TNSRE.2019.2939193
DO - 10.1109/TNSRE.2019.2939193
M3 - Article
C2 - 31494552
AN - SCOPUS:85073668493
SN - 1534-4320
VL - 27
SP - 2118
EP - 2127
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 10
M1 - 8823953
ER -